|Manifold Preserving: An Intrinsic Approach for Semisupervised Distance Metric Learning|
|Shihui Ying; Zhijie Wen; Jun Shi; Yaxin Peng; Jigen Peng; Hong Qiao
|Source Publication||IEEE Transactions on Neural Networks and Learning Systems
|Abstract||In this paper, we address the semisupervised distance metric learning problem and its applications in classification and image retrieval. First, we formulate a semisupervised distance metric learning model by considering the metric information of inner classes and interclasses. In this model, an adaptive parameter is designed to balance the inner metrics and intermetrics by using data structure. Second, we convert the model to a minimization problem whose variable is symmetric positive-definite matrix. Third, in implementation, we deduce an intrinsic steepest descent method, which assures that the metric matrix is strictly symmetric positive-definite at each iteration, with the manifold structure of the symmetric positivedefinite matrix manifold. Finally, we test the proposed algorithm on conventional data sets, and compare it with other four representative methods. The numerical results validate that the proposed method significantly improves the classification with the same computational efficiency.|
Distance Metric Learning
Shihui Ying,Zhijie Wen,Jun Shi,et al. Manifold Preserving: An Intrinsic Approach for Semisupervised Distance Metric Learning[J]. IEEE Transactions on Neural Networks and Learning Systems,2017,PP(99):1-12.
Shihui Ying,Zhijie Wen,Jun Shi,Yaxin Peng,Jigen Peng,&Hong Qiao.(2017).Manifold Preserving: An Intrinsic Approach for Semisupervised Distance Metric Learning.IEEE Transactions on Neural Networks and Learning Systems,PP(99),1-12.
Shihui Ying,et al."Manifold Preserving: An Intrinsic Approach for Semisupervised Distance Metric Learning".IEEE Transactions on Neural Networks and Learning Systems PP.99(2017):1-12.
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